“Moving from understanding to action on food security in Inuit Nunangat”:
Bibliographic record
Abstract
This Commentary details key challenges and opportunities relating to the promotion of food security in Inuit Nunangat, discussed as part of the event “Moving from understanding to action on food security in Inuit Nunangat”, convened at the ArcticNet Annual Scientific Meeting on 5th December 2022 in Toronto. The purpose of the event was to explore opportunities for action on food security in northern communities, and to mobilize knowledge on current and future food security programming. A range of stakeholders from across Inuit Nunangat and Canada were involved, including representatives from Inuit Tapiriit Kanatami and Nutrition North Canada, territorial, regional, and community food security co-ordinators and government delegates, academics, and community members. Points of discussion across the day included the integration of culturally appropriate country foods into food programming; the importance of human and financial resources to program success; interactions between COVID-19, climate change, and food security; challenges relating to the classification of “households” in food security surveys; and the crucial importance of school food programs for reducing food and income stress on families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.021 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.015 | 0.017 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".